基于磁记忆的应力集中神经网络识别  

Neural Network Recognition of Stress Concentration Based on Magnetic Memory Testing

作　　者：王慧鹏[1] 董丽虹[1] 董世运[1] 徐滨士[1] 

机构地区：[1]装甲兵工程学院装备再制造技术国防科技重点实验室,北京100072

出　　处：《理化检验（物理分册）》2013年第9期576-579,共4页Physical Testing and Chemical Analysis(Part A:Physical Testing)

基　　金：国家自然科学基金项目(50975283;50975287);国家973课题(2011CB013401);装备维修改革项目(2011WG07)

摘　　要：为了探索磁记忆检测技术定量表征工件应力集中程度的方法,加工制备了不同应力集中系数的42CrMo钢试样进行拉压疲劳试验,采用磁记忆检测仪器测量不同疲劳周次时试样表面的法向和切向磁记忆信号。确定了不同应力集中程度下磁记忆信号的特征参量,并以此作为输入特征向量建立了BP神经网络,对试样的应力集中程度进行定量识别。结果表明:利用建立的BP神经网络能够实现试样应力集中程度的定量识别。To explore a properly method for characterizing stress concentration degree quantitatively by metal magnetic memory testing （MMMT）, tension-compression fatigue tests of specimens with different stress concentration factors made of 42CrMo steel were carried out. Both normal and tangential component of magnetic memory signals of specimens under different fatigue cycles were measured by magnetic memory apparatus. A back propagation neural network （BP neural network） was built to distinguish the stress concentration degree, whose input eigenvector was the feature extracted from magnetic memory signals. The results showed that the BP neural network could be used to recognize stress concentration degree of specimens quantitatively.

关 键 词：金属磁记忆检测 磁记忆信号 应力集中 特征提取 BP神经网络 

分 类 号：TG142.41[一般工业技术—材料科学与工程]